By Jessica Collier and Michael Poplawski, Pacific Northwest National Laboratory
In today’s construction process, accountability is primarily defined - and limited by - legal obligations. How can digital tools and workflows increase and broaden accountability? This article offers some proposals on how that question might be answered, driven by a thought exercise that resulted in the following knowledge graph.
Despite the steady development of experience-based practice and sophisticated software tools, the architectural design process remains inefficient. Inaccurate or irrelevant simulation assumptions, several rounds of submittal revisions, and redundant 2D and 3D document coordination inherently limit the efficiency of the process and ultimately influence the performance of the building systems. The project owner and the building design team (e.g., architects, engineers, consultants) essentially end all communication once the building is occupied. The design team may be available to resolve the remaining punch list items for the first several months of operation. However, addressing any longer-term performance shortcomings, degraded materials, or occupant complaints is not the design team's responsibility. The design team is contractually obligated to provide technically accurate plans, specifications, and the interpretation of those documents (i.e., design intent), to ensure code compliance and review submittals. The design team's obligations typically end after the construction administration period, including a preliminary evaluation of the contractor's work on-site, but rarely includes a meeting with the operations team. Therefore, the accountability and responsibility of the design team typically end within the first days of building occupancy. As a result, the variation between the intended building performance and actual building performance is largely unknown.
The cause of many of these inefficiencies is the limited data exchange between professionals, across software platforms, and throughout the design, construction, and operation phases. This lack of communication and information exchange creates an environment where members of the design or construction teams continuously make uninformed decisions based on outdated or inaccurate data. The lack of alignment between design professionals complicates and delays decisions regarding system integration, interoperability, and cybersecurity. Although there is a large amount of information generated during the design process, design professionals tend to have minimal working knowledge about the long-term operation of buildings. There is no widely adopted feedback loop or mechanism for design professionals to learn how the systems they specify influence operating costs or occupant satisfaction. This flawed practice has significant ramifications. While the design team should be responsible for their decision-making outcomes, they are instead incentivized to focus on delivering “design intent” and limit accountability, leading to tightly bound contracts and potentially higher project costs to avoid risk. This limited accountability has become exacerbated with the advent of LED technology, which offers exponentially more design options and decisions.
Digital Twins present a promising means for improving data exchange and accountability in architectural applications. A Digital Twin for a building might capture all information generated during design into a shared digital environment and facilitate knowledge transfer between project phases to make informed decisions in a transparent environment. Further, the Twin can harness and associate the vast amount of data generated by building systems, sensors, and IoT devices and facilitate the comparison of design "inputs" with operational "outputs." Designers could develop an objective understanding of building performance and leverage it in future designs if this feedback loop integrates it with standard practice. Streamlining the capture, validation, and analysis of data will ultimately improve building system performance, occupant satisfaction, and designer accountability. Let’s review each capability in more detail.
Structured collaboration and continuous design validation can facilitate accountability and improve building performance.
The first step towards improving accountability requires that design professionals capture all aspects of their design in a shared data environment, including how they applied their domain-specific expertise (e.g., objective and subjective criteria used to make design decisions). Over time, this shared data environment becomes a robust and detailed record that increases accuracy throughout the design. It promotes transparency and collaboration while maintaining original authorship and encouraging trust instead of risk-aversion. For example, building simulations are completed by establishing assumptions that, along with the results, are typically contained within the simulation software itself. The shared data environment allows the entire project team to access assumptions, results, and outcomes completed by other disciplines, which increases project knowledge across the team and improves accuracy in their domain-specific work.
Aggregating data in a shared environment can also provide a documented series of validations and verifications as the design progresses. Digital, automated verification of code compliance measures or performance targets throughout the design process can reduce redundant work that often occurs when design professionals operate independently of one another. For example, surface finishes are vital for lighting design; however, finishes may change frequently or are specified after completing most lighting. The shared data environment should capture a record of surface finishes associated with other simulations or calculations in the shared workspace. Working in a structured collaboration environment ensures that design professionals always have access to the latest information and direct their attention. The current design is not meeting design criteria or code compliance as the design becomes complete.
Deployment of data-reporting systems can allow for direct comparison between design targets and resulting performance.
Building systems are increasingly capable of monitoring and reporting their performance. Once such systems are operational, the digital design can serve as a fully and accurately structured landing pad for incoming data from these systems and other deployed sensors. These data can be used to evaluate whether project goals and performance parameters specified during design are met and to identify and address any gaps between expectations and reality. In addition, determining key performance indicators (which may or may not exist today) that can be simulated during design and measured during operation will increase the likelihood of identifying significant gaps between expected and actual performance.
Advances in computing power, technology, and communications have substantially reduced the time and resources necessary to analyze large data sets and draw intelligent, optimized conclusions. In addition to data aggregation, the shared data environment might store defined logical relationships and models and utilize machine learning to fine-tune operational strategies.
Leveraging operational data during design can support decision-making and improve standard practice.
Data collected from the real building can improve workflows and update best practices in response to real scenarios, thereby closing the feedback loop between design and operation. Building systems or architectural features are rarely monitored or evaluated post-occupancy to determine application efficacy, which leaves project owners and stakeholders unsure of whether their investment met the design goals. Further, the design professional remains unclear whether their design choices are intended and meet owner goals and occupant needs. Digital Twins can provide evidence to support or negate decisions moving forward regarding both standard or repetitive practices and unique design problems and applications alike.
In addition to building system (e.g., lighting, HVAC) performance, data collected and aggregated from various non-building system sources, including occupant complaints, maintenance logs, or room scheduling applications, can inform previously intangible design concepts like occupant satisfaction or comfort. Integrated data associations will begin to break the silos between design professionals by allowing them to define design data within their scope of work actively and ingest peripherally relevant data. Understanding the holistic consequences of every decision can improve overall building performance, just as building systems, occupants, other architectural elements, and unpredictable factors (e.g., usage, weather) do not exist in isolation in the real world. Building owners and designers alike can evaluate design decisions in new ways, considering things like cost (capital, operating, and maintenance), interoperability maturity, occupant productivity, and tenant experience.
Digital Twins can increase accountability by closing the gap between building design and resulting performance.
Buildings need to be less expensive and time-intensive to design and construct and perform better. Quantitatively comparing design intent with real-world performance can empower design professionals to make holistic decisions regarding energy efficiency, material waste, and tradeoffs between capital and operating expenses. For example, which can increase confidence in design choices and close the gap between design and resulting performance. Digital Twins can improve building performance, occupant satisfaction, and accountability across the lifecycle of a building. Beginning in the design phase allows for increased communication between trades, informed modeling, and integrated design. The Digital Twin’s ability to capture, analyze, validate, and associate large amounts of building performance data provides the body of evidence needed to support decisions making for all stakeholders.
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